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The Multi–Rate Support feature has been introduced by the IEEE 802.11 standard to improve system performance, and has been widely exploited by means of Rate Adaptation (RA) strategies within general purpose Wireless LANs. These strategies revealed ineffective for real–time industrial communications, and alternative solutions, better tailored for such a specific field of application, were investigated. The preliminary outcomes of the analyses carried out were promising, even if they clearly indicated that further efforts were necessary. In this direction, this paper firstly proposes Rate Selection for Industrial Networks (RSIN), an innovative RA algorithm specifically conceived for the real–time industrial scenario withthe goal of minimizing the transmission error probability, while taking into account the deadline imposed to packet delivery. Then, it describes the practical implementation of RSIN on commercial devices, along with that of other formerly introduced RA techniques. Finally, the paper presents a thorough performanceanalysis, carried out to investigate the behavior of the addressed RA schemes. Such an assessment was performed via both experimental campaigns and simulations. The obtained results, on the one hand, confirm the effectiveness of the RA techniques purposely designed for real–time industrial communication. On the other hand, they clearly indicate that RSIN outperforms all the other strategies.

Within the context of multi-agent systems, the
distributed state estimation problem constitutes a key issue in
several application scenarios. The most challenging aspect of
this problem relies on the fact that the agents are generally sup-
posed to be able to only access and exchange relative noisy mea-
surements. By leveraging the standard least-squares approach,
in this work we face such an estimation task by deriving a
distributed ADMM-based iterative scheme. This solution entails
the emergence of interesting connections between the structural
properties of the stochastic matrices describing the network
system dynamics and the convergence behavior towards the
optimal estimate. These relations are deeply investigated in the
paper, where also a discussion is provided on the tuning of the
penalty parameter that characterizes the ADMM dynamics.

In fast nonlinear model predictive control the sensitivitycomputation is one of the key aspects to reducecomputational burden, in fact specific automated andefficient procedures for that have been developed. Howeverthe number of sensitivity computations required toadequately approximate the nonlinear dynamics is typicallyhigh and fixed a priori. In this paper, we developa sensitivity updating scheme capable of reducing thenumber of sensitivity computations exploiting an onlinecurvature-based measure of nonlinearity of the system.The proposed strategy is applied to the sequentialquadratic programming framework with specific attentionto the Real-Time Iteration implementation. Simulationson the inverted pendulum benchmark show asignificant reduction of the number of the sensitivity updates,hence a reduction of the overall computationaltime.

In this work we focus on the problem of minimizing the sum of convex cost functions in a distributed fashion over a peer-to-peer network. In particular we are interested in the case in which communications between nodes are lossy and the agents are not synchronized among themselves. We address this problem by proposing a modified version of the relaxed ADMM (R-ADMM), which corresponds to the generalized Douglas-Rachford operator applied to the dual of our problem. By exploiting results from operator theory we are then able to prove the almost sure convergence of the proposed algorithm under i.i.d. random packet losses and asynchronous operation of the agents. By further assuming the cost functions to be strongly convex, we are able to prove that the algorithm converges exponentially fast in mean square in a neighborhood of the optimal solution. Moreover, we provide an upper bound to the convergence rate. Finally, we present numerical simulations of the proposed algorithm over random geometric graphs in the aforementioned lossy and asynchronous scenario.

The last decade has seen tremendous improvements in technologies for Type 1 Diabetes (T1D) management, in particular the so-called artificial pancreas (AP), a wearable closed-loop device modulating insulin injection based on glucose sensor readings. Unluckily, the AP actuator, an insulin pump, is subject to failures, with potentially serious consequences for subject safety. This calls for the development of advanced monitoring systems, leveraging the unprecedented data availability. This paper tackles for the first time the problem of automatically detecting pump faults with multidimensional data-driven anomaly detection (AD) methodologies. The approach allows to avoid the subtask of identifying a physiological model, typical of model-based approaches. Furthermore, we employ unsupervised methods, removing the need of labeled data for training, hardly available in practice. The adopted data-driven AD methods are local outlier factor, connectivity-based outlier factor, and isolation forest. Moreover, we propose a modification of these methods to cope with the dynamic nature of the underlying problem. The algorithms were tuned and tested on: 1) two-synthetic 100-patients' data set, of one-month data each, generated using the ``UVA/Padova T1D Simulator,'' a large-scale nonlinear computer simulator of T1D subject physiology, largely adopted in AP research and accepted by the American Food and Drug Administration as a replacement of preclinical animal trials for AP and 2) a real 7-patients' data set consisting of one month in free-living conditions. The satisfactory accuracy of the proposed approach paves the way to the embedding of these methodologies in AP systems or their deployment in remote monitoring systems.

In this paper we tackle the localization problem
for a visual sensor network, providing a distributed solution
inspired by [1]. Adopting a similar optimization framework,
we propose an estimation scheme that exploits the unit dual
quaternion algebra to describe the sensors pose. This represen-
tation choice allows to solve the localization problem without
designing two consecutive position and orientation estimators,
thus improving the estimation error distribution over the two
pose components. Furthermore, numerical results asserts the
robustness of the proposed algorithm w.r.t. the initial conditions.

In this work, it is presented the development of
an efficient algorithm performing robotic coverage, clustering
and dispatch around an event in static-obstacle-structured
environments without relying on metric information. Specif-
ically, the aim is to account for the trade-off between local
communication given by bearing visibility sensors installed on
each agent involved, optimal deployment in closed unknown
scenarios and focus of a group of agents on one point of interest.
The particular targets of this study can be summarized as 1.
the computation, under certain topological assumptions, of a
lower bound for the number of required agents, which are
provided by a realistic geometric model (e.g. a round shape)
to emphasize physical limitations; 2. the minimization of the
number of nodes and links maintaining a distributed approach
over a connected communication graph; 3. the identification of
an activation cluster around an event with a radial decreasing
intensity, sensed by each agent; 4. the attempt to send the agents
belonging to the cluster towards the most intense point in the
scenario by minimizing a weighted isoperimetric functional.

Aerial robotics is increasingly becoming an at-
tractive field of research thanks to the peculiar mixture of
theoretical issues to be solved and technological challenges
to be faced. In particular, recent developments have seen the
multiplication of multi-rotor platforms that aim at improving
the maneuverability of classical quad-rotors in standard and
harsh flying conditions, thus opening the field to compre-
hensive studies over the structural multi-rotor properties of
actuation, decoupling and robustness, which strongly depend
on the mechanical configuration of the systems. This work
collocates along this line of research by considering star-shaped
generically-tilted multi-rotors (SGTMs), namely platforms with
more than four possibly tilted propellers (along two tilting
orthogonal axes, namely dual-tilted). For these platforms, we
investigate how the structural choices over the number of
the propellers and the tilting angles affect the force-moment
decoupling features and, by recalling the robustness definition
that refers to the hovering capabilities of the platform, we
provide a robustness analysis and an hoverability assessment
for SGTMs having five to eight actuators against the loss of
one and two propellers.

This work deals with formations of mobile agents
having six independently controllable degrees of freedom and
able to retrieve relative bearing measurements w.r.t. their neigh-
bors in the group. Exploiting the bearing rigidity framework,
two control objectives are here addressed: (i) the stabilization
of such fully actuated multi-agent systems towards desired con-
figurations, and (ii) their coordinated motion along directions
guaranteeing the system shape maintenance. The proposed ap-
proach relies on a new formulation of the bearing rigidity theory
based on the adoption of the unit quaternion formalism to
describe the agents attitude. Through this representation choice,
the formation dynamics is linear w.r.t. the input control veloci-
ties and the rigidity theory suggests the design of a distributed
control scheme for both formation stabilization and collective
motion whose efficacy is confirmed by numerical simulations.

We consider the hovering control problem for a class of multi-rotor aerial platforms with generically oriented propellers. Given
the intrinsically coupled translational and rotational dynamics of such vehicles, we first discuss some assumptions for the
considered systems to reject moment disturbances and to balance the gravity force, which are translated into a geometric
characterization of the platforms that is usually fulfilled by both standard models and more general configurations. Hence,
we propose a control strategy based on the identification of a zero-moment direction for the applied force and the dynamic
state feedback linearization around this preferential direction, which allows to asymptotically stabilize the platform to a static
hovering condition. Stability and convergence properties of the control law are rigorously proved through Lyapunov-based
methods and reduction theorems for the stability of nested sets. Asymptotic zeroing of the error dynamics and convergence to
the static hovering condition are then confirmed by simulation results on a star-shaped hexarotor model with tilted propellers.

The state estimation of a multi-agent system
resting upon noisy measurements constitutes a problem re-
lated to several applicative scenarios, such as, for example,
robotic localization and navigation, resource balancing and task
allocation, cooperative manipulation and coordinated control.
Adopting the standard least-squares approach, in this work
we derive both the (centralized) analytic solution to this issue
and two distributed iterative schemes, which allow to establish
a connection between the convergence behavior of consensus
algorithm towards the optimal estimate and the theory of the
stochastic matrices that describe the network system dynamics.
This study on the one hand highlights the role of the topological
links that define the neighborhood of agent nodes, while on the
other allows to optimize the convergence rate by easy parameter
tuning. The theoretical findings are validated considering dif-
ferent network topologies by means of numerical simulations.

In the last years, IEEE 802.11 Wireless LANs (WLANs) have proved their
eectiveness for a wide range of real– time industrial communication
applications. Nonetheless, the introduction of the important IEEE
802.11n amendment, which is commonly implemented in commercial devices,
has not been adequately addressed in this operational framework yet.
IEEE 802.11n encompasses several enhancements both at the physical (PHY)
and medium access control (MAC) layers that may bring considerable
improvements to the performance of WLANs deployed in real–time
industrial communication systems. To this regard, in this paper we
present a thorough investigation of the most important IEEE 802.11n
features, addressing in particular specific performance indicators, such
as timeliness and reliability, that are crucial for industrial
communication systems. To this aim, after an accurate theoretical
analysis, we implemented a suitable experimental setup and carried out
several measurement sessions to obtain an exhaustive performance
assessment. The outcomes of these experiments, on the one hand revealed
that the adoption of IEEE 802.11n can actually provide significant
improvements to the performance of the IEEE 802.11 WLAN in the
industrial communication scenario. On the other hand, the assessment
allowed to select, among the various options of IEEE 802.11n, the
parameter settings which may ensure the best behavior in this specific
(and demanding) field of application.

Given a multi-agent linear system, we formalize
and solve a trajectory optimization problem that encapsulates
trajectory tracking, distance-based formation control and input
energy minimization. To this end, a numerical projection
operator Newton’s method is developed to find a solution by
the minimization of a cost functional able to capture all these
different tasks. To stabilize the formation, a particular poten-
tial function has been designed, allowing to obtain specified
geometrical configurations while the barycenter position and
velocity of the system follows a desired trajectory.

We study the solution of a time-varying optimization problem which is observed, that is, it is known, only intermittently. We propose three approaches based on the prediction-correction scheme for solving this problem by exploiting splitting methods. We present convergence results in mean to a bounded asymptotical error, and showcase them in a numerical example featuring a regression problem.

Nowadays, vehicle flow monitoring, model-based
traffic management, and congestion prediction are becoming
fundamental elements for the realization of the Smart City
paradigm. These tasks usually require wide sensor deploy-
ments, but, due to economical, practical, and environmental
constraints, they must be accomplished with a limited number
of sensors. Thus motivated, this work addresses the sensors
selection problem for urban street monitoring, by employing
a road map image as the basic information and considering
the placement of at most one sensor along each road with a
chosen number of available devices. To solve the problem, the
concept of system observability is exploited as the criterium for
optimal sensor placement, specifically related to the capability
of estimating the traffic flow in each road using the available
output measurements. In this framework, different integer non-
linear programming problems are proposed, whose solutions
are studied and analyzed by means of numerical simulations
on a real case scenario.

Synchronization is crucial for the correct func-tionality of many natural and man-made complex systems. Inthis work we characterize the formation of synchronizationpatterns in networks of Kuramoto oscillators. Specifically, wereveal conditions on the network weights and structure and onthe oscillators’ natural frequencies that allow the phases of agroup of oscillators to evolve cohesively, yet independently fromthe phases of oscillators in different clusters. Our conditionsare applicable to general directed and weighted networks ofheterogeneous oscillators. Surprisingly, although the oscillatorsexhibit nonlinear dynamics, our approach relies entirely ontools from linear algebra and graph theory. Further, we developa control mechanism to determine the smallest (as measuredby the Frobenius norm) network perturbation to ensure theformation of a desired synchronization pattern. Our procedureallows to constrain the set of edges that can be modified, thusenforcing the sparsity structure of the network perturbation.The results are validated through a set of numerical examples.

In industrial automation the installation
of wireless networks is growing and their use for trans-
mission on safety relevant data is becoming appealing.
In this paper we propose an implementation of the
Fail Safe over EtherCAT (FSoE) protocol on the top
of IEEE 802.11 WLAN. In particular the paper gives
a brief introduction to FSoE, some detail about the
hardware and software architecture for encapsulation
of safety PDUs into UDP frames and analyze the
performances of the implemented protocol on an ex-
perimental set–up with a focus on packet loss and end–
to–end time

Industrial applications aimed at real–time control and monitoring of cyber–physical systems pose significant challenges to the underlying communication networks in terms of determinism, low latency and high reliability. The migration of these networks from wired to wireless could bring several benefits in terms of cost reduction and simplification of design, but currently available wireless techniques cannot cope with the stringent requirements of the most critical applications. In this work, we consider the problem of designing a high–performance wireless network for industrial control, targeting at Gbps data rates and 10 ?s–level cycle time. To this aim, we start from analysing the required performance and deployment scenarios, then we take a look at the most advanced standards and emerging trends that may be applicable. Building on this investigation, we outline the main directions for the development of a wireless high performance system.

We propose a novel approach for the inspection of metallic surfaces, integrable in the production phase. It consists of
a compact illumination and vision equipment that projects over a moving object a series of light bands. We developed a
specific feature extraction algorithms based on the dynamic evolution of the reflected light over the object surface, and we
built an Hybrid Learning System by feeding an Auto-Encoder CNN with this dynamic light features. The results obtained by
this novel approach reach higher performance respect classic Deep Learning networks and Machine Learning technique,
in critical light conditions too.